Temporal Convolutional Networks:
Temporal convolutional networks (TCN) [1]}[2]} can jointly handle spatial and temporal features hierarchically without the explosion of parameter count, memory footprint, layer count, or overfitting. TCN convolves only on current and past elements from earlier layers but not future inputs, thereby maintaining temporal ordering without requiring recurrent connections. Dilated kernels allow the network to discover semantic connections
in long temporal sequences while increasing network capacity and receptive field size with fewer parameters or layers over vanilla RNN. Two TCN layers are fused through a gated residual connection for expressive non-linearity and temporal correlation modeling. A time-series TCN can be 100\(\times \)  smaller over a CNN-LSTM [3]}[4]}. TCN also supports parallel and out-of-order training.
